Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations11244
Missing cells0
Missing cells (%)0.0%
Duplicate rows25
Duplicate rows (%)0.2%
Total size in memory2.1 MiB
Average record size in memory199.5 B

Variable types

Numeric15
Categorical6

Alerts

Dataset has 25 (0.2%) duplicate rowsDuplicates
CLASIFICACION_BECAS is highly overall correlated with PORCENTAJE_BECA_INTERNA_CARRERAHigh correlation
EDAD is highly overall correlated with YEAR_GRADUATIONHigh correlation
PCA is highly overall correlated with PCA_max and 2 other fieldsHigh correlation
PCA_max is highly overall correlated with PCA and 1 other fieldsHigh correlation
PCA_min is highly overall correlated with PCA and 2 other fieldsHigh correlation
PORCENTAJE_BECA_INTERNA_CARRERA is highly overall correlated with CLASIFICACION_BECASHigh correlation
PROMEDIO_GLOBAL is highly overall correlated with PROMEDIO_GLOBAL_max and 1 other fieldsHigh correlation
PROMEDIO_GLOBAL_max is highly overall correlated with PROMEDIO_GLOBAL and 1 other fieldsHigh correlation
PROMEDIO_GLOBAL_min is highly overall correlated with PROMEDIO_GLOBAL and 1 other fieldsHigh correlation
YEAR_GRADUATION is highly overall correlated with EDADHigh correlation
num_semesters is highly overall correlated with std_PGA_changeHigh correlation
std_PCA_change is highly overall correlated with PCA and 1 other fieldsHigh correlation
std_PGA_change is highly overall correlated with num_semestersHigh correlation
TIPO_ESTADO_CIVIL is highly imbalanced (60.9%) Imbalance
Times_returned is highly imbalanced (80.1%) Imbalance
ESTADO_ESTUDIANTE is highly imbalanced (51.2%) Imbalance
Cumulative_Missed_Semesters has 10476 (93.2%) zeros Zeros
PROMEDIO_GLOBAL_min has 203 (1.8%) zeros Zeros
std_PGA_change has 5662 (50.4%) zeros Zeros
std_PCA_change has 10641 (94.6%) zeros Zeros
avg_PGA_trend_per_semester has 2446 (21.8%) zeros Zeros
avg_PCA_trend_per_semester has 10428 (92.7%) zeros Zeros
PORCENTAJE_BECA_INTERNA_CARRERA has 8859 (78.8%) zeros Zeros

Reproduction

Analysis started2024-11-28 21:47:25.354177
Analysis finished2024-11-28 21:48:22.402941
Duration57.05 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

EDAD
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.116951
Minimum23
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:22.587059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile28
Q131
median35
Q340
95-th percentile49
Maximum74
Range51
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.6586857
Coefficient of variation (CV)0.18436456
Kurtosis1.1464701
Mean36.116951
Median Absolute Deviation (MAD)4
Skewness1.001574
Sum406099
Variance44.338096
MonotonicityNot monotonic
2024-11-28T16:48:22.846792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
33 911
 
8.1%
32 891
 
7.9%
31 815
 
7.2%
30 778
 
6.9%
35 764
 
6.8%
34 747
 
6.6%
36 633
 
5.6%
37 540
 
4.8%
29 482
 
4.3%
38 475
 
4.2%
Other values (38) 4208
37.4%
ValueCountFrequency (%)
23 15
 
0.1%
24 51
 
0.5%
25 101
 
0.9%
26 131
 
1.2%
27 195
 
1.7%
28 358
3.2%
29 482
4.3%
30 778
6.9%
31 815
7.2%
32 891
7.9%
ValueCountFrequency (%)
74 1
 
< 0.1%
71 2
 
< 0.1%
69 1
 
< 0.1%
68 2
 
< 0.1%
67 1
 
< 0.1%
65 4
< 0.1%
64 1
 
< 0.1%
63 6
0.1%
62 9
0.1%
61 6
0.1%

TIPO_ESTADO_CIVIL
Categorical

Imbalance 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
SOLTERO
8875 
CASADO
1566 
UNION LIBRE
 
633
DIVORCIADO
 
87
SEPARADO
 
73

Length

Max length11
Median length7
Mean length7.1138385
Min length5

Characters and Unicode

Total characters79988
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSOLTERO
2nd rowCASADO
3rd rowSOLTERO
4th rowSOLTERO
5th rowSOLTERO

Common Values

ValueCountFrequency (%)
SOLTERO 8875
78.9%
CASADO 1566
 
13.9%
UNION LIBRE 633
 
5.6%
DIVORCIADO 87
 
0.8%
SEPARADO 73
 
0.6%
VIUDO 10
 
0.1%

Length

2024-11-28T16:48:23.102696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T16:48:23.315050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
soltero 8875
74.7%
casado 1566
 
13.2%
union 633
 
5.3%
libre 633
 
5.3%
divorciado 87
 
0.7%
separado 73
 
0.6%
viudo 10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
O 20206
25.3%
S 10514
13.1%
R 9668
12.1%
E 9581
12.0%
L 9508
11.9%
T 8875
11.1%
A 3365
 
4.2%
D 1823
 
2.3%
C 1653
 
2.1%
I 1450
 
1.8%
Other values (6) 3345
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 79355
99.2%
Space Separator 633
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 20206
25.5%
S 10514
13.2%
R 9668
12.2%
E 9581
12.1%
L 9508
12.0%
T 8875
11.2%
A 3365
 
4.2%
D 1823
 
2.3%
C 1653
 
2.1%
I 1450
 
1.8%
Other values (5) 2712
 
3.4%
Space Separator
ValueCountFrequency (%)
633
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79355
99.2%
Common 633
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 20206
25.5%
S 10514
13.2%
R 9668
12.2%
E 9581
12.1%
L 9508
12.0%
T 8875
11.2%
A 3365
 
4.2%
D 1823
 
2.3%
C 1653
 
2.1%
I 1450
 
1.8%
Other values (5) 2712
 
3.4%
Common
ValueCountFrequency (%)
633
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 20206
25.3%
S 10514
13.1%
R 9668
12.1%
E 9581
12.0%
L 9508
11.9%
T 8875
11.1%
A 3365
 
4.2%
D 1823
 
2.3%
C 1653
 
2.1%
I 1450
 
1.8%
Other values (6) 3345
 
4.2%
Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
INGENIERÍA
3985 
ADMINISTRACIÓN
2125 
EDUCACIÓN
1011 
CIENCIAS SOCIALES
890 
DERECHO
751 
Other values (8)
2482 

Length

Max length30
Median length29
Mean length12.032373
Min length5

Characters and Unicode

Total characters135292
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDERECHO
2nd rowEDUCACIÓN
3rd rowDERECHO
4th rowDERECHO
5th rowINGENIERÍA

Common Values

ValueCountFrequency (%)
INGENIERÍA 3985
35.4%
ADMINISTRACIÓN 2125
18.9%
EDUCACIÓN 1011
 
9.0%
CIENCIAS SOCIALES 890
 
7.9%
DERECHO 751
 
6.7%
ECONOMÍA 603
 
5.4%
ESC DE GOBIERNO ALBERTO LLERAS 502
 
4.5%
CIENCIAS 470
 
4.2%
ARTES Y HUMANIDADES 375
 
3.3%
CIDER 311
 
2.8%
Other values (3) 221
 
2.0%

Length

2024-11-28T16:48:23.602938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ingeniería 3985
26.2%
administración 2125
14.0%
ciencias 1360
 
8.9%
educación 1011
 
6.6%
sociales 890
 
5.8%
derecho 751
 
4.9%
economía 603
 
4.0%
y 536
 
3.5%
alberto 502
 
3.3%
lleras 502
 
3.3%
Other values (13) 2952
19.4%

Most occurring characters

ValueCountFrequency (%)
I 20596
15.2%
E 17291
12.8%
N 16132
11.9%
A 14609
10.8%
C 10147
7.5%
R 9377
 
6.9%
S 7180
 
5.3%
D 5671
 
4.2%
Í 4588
 
3.4%
O 4516
 
3.3%
Other values (17) 25185
18.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 131315
97.1%
Space Separator 3973
 
2.9%
Other Punctuation 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 20596
15.7%
E 17291
13.2%
N 16132
12.3%
A 14609
11.1%
C 10147
7.7%
R 9377
7.1%
S 7180
 
5.5%
D 5671
 
4.3%
Í 4588
 
3.5%
O 4516
 
3.4%
Other values (13) 21208
16.2%
Space Separator
ValueCountFrequency (%)
3973
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 131315
97.1%
Common 3977
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 20596
15.7%
E 17291
13.2%
N 16132
12.3%
A 14609
11.1%
C 10147
7.7%
R 9377
7.1%
S 7180
 
5.5%
D 5671
 
4.3%
Í 4588
 
3.5%
O 4516
 
3.4%
Other values (13) 21208
16.2%
Common
ValueCountFrequency (%)
3973
99.9%
. 2
 
0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 127407
94.2%
None 7885
 
5.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 20596
16.2%
E 17291
13.6%
N 16132
12.7%
A 14609
11.5%
C 10147
8.0%
R 9377
7.4%
S 7180
 
5.6%
D 5671
 
4.5%
O 4516
 
3.5%
G 4487
 
3.5%
Other values (14) 17401
13.7%
None
ValueCountFrequency (%)
Í 4588
58.2%
Ó 3136
39.8%
Ñ 161
 
2.0%

PROMEDIO_GLOBAL
Real number (ℝ)

High correlation 

Distinct247
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2463189
Minimum0
Maximum5
Zeros56
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:24.102997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.7
Q14.11
median4.31
Q34.48
95-th percentile4.72
Maximum5
Range5
Interquartile range (IQR)0.37

Descriptive statistics

Standard deviation0.46412468
Coefficient of variation (CV)0.10930048
Kurtosis37.129054
Mean4.2463189
Median Absolute Deviation (MAD)0.19
Skewness-4.7735408
Sum47745.61
Variance0.21541172
MonotonicityNot monotonic
2024-11-28T16:48:24.425160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.36 178
 
1.6%
4.31 176
 
1.6%
4.4 174
 
1.5%
4.5 169
 
1.5%
4.33 169
 
1.5%
4.3 168
 
1.5%
4.45 168
 
1.5%
4.32 166
 
1.5%
4.34 163
 
1.4%
4.43 160
 
1.4%
Other values (237) 9553
85.0%
ValueCountFrequency (%)
0 56
0.5%
1.5 23
0.2%
1.55 1
 
< 0.1%
1.56 1
 
< 0.1%
1.6 1
 
< 0.1%
1.61 2
 
< 0.1%
1.75 1
 
< 0.1%
1.77 1
 
< 0.1%
1.78 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5 17
0.2%
4.99 1
 
< 0.1%
4.95 2
 
< 0.1%
4.94 4
 
< 0.1%
4.92 10
0.1%
4.91 11
0.1%
4.9 18
0.2%
4.89 4
 
< 0.1%
4.88 13
0.1%
4.87 15
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
BOGOTÁ, D.C.
9495 
FUERA DE BOGOTÁ, D.C.
1749 

Length

Max length21
Median length12
Mean length13.399947
Min length12

Characters and Unicode

Total characters150669
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBOGOTÁ, D.C.
2nd rowBOGOTÁ, D.C.
3rd rowBOGOTÁ, D.C.
4th rowBOGOTÁ, D.C.
5th rowBOGOTÁ, D.C.

Common Values

ValueCountFrequency (%)
BOGOTÁ, D.C. 9495
84.4%
FUERA DE BOGOTÁ, D.C. 1749
 
15.6%

Length

2024-11-28T16:48:24.696364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T16:48:24.904932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
bogotá 11244
43.3%
d.c 11244
43.3%
fuera 1749
 
6.7%
de 1749
 
6.7%

Most occurring characters

ValueCountFrequency (%)
O 22488
14.9%
. 22488
14.9%
14742
9.8%
D 12993
8.6%
B 11244
7.5%
G 11244
7.5%
T 11244
7.5%
Á 11244
7.5%
, 11244
7.5%
C 11244
7.5%
Other values (5) 10494
7.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 102195
67.8%
Other Punctuation 33732
 
22.4%
Space Separator 14742
 
9.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 22488
22.0%
D 12993
12.7%
B 11244
11.0%
G 11244
11.0%
T 11244
11.0%
Á 11244
11.0%
C 11244
11.0%
E 3498
 
3.4%
F 1749
 
1.7%
U 1749
 
1.7%
Other values (2) 3498
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 22488
66.7%
, 11244
33.3%
Space Separator
ValueCountFrequency (%)
14742
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102195
67.8%
Common 48474
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 22488
22.0%
D 12993
12.7%
B 11244
11.0%
G 11244
11.0%
T 11244
11.0%
Á 11244
11.0%
C 11244
11.0%
E 3498
 
3.4%
F 1749
 
1.7%
U 1749
 
1.7%
Other values (2) 3498
 
3.4%
Common
ValueCountFrequency (%)
. 22488
46.4%
14742
30.4%
, 11244
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139425
92.5%
None 11244
 
7.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 22488
16.1%
. 22488
16.1%
14742
10.6%
D 12993
9.3%
B 11244
8.1%
G 11244
8.1%
T 11244
8.1%
, 11244
8.1%
C 11244
8.1%
E 3498
 
2.5%
Other values (4) 6996
 
5.0%
None
ValueCountFrequency (%)
Á 11244
100.0%

PCA
Real number (ℝ)

High correlation 

Distinct187
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98100799
Minimum0
Maximum1
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:25.188150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8974359
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.095670601
Coefficient of variation (CV)0.097522753
Kurtosis68.038286
Mean0.98100799
Median Absolute Deviation (MAD)0
Skewness-7.6984107
Sum11030.454
Variance0.0091528638
MonotonicityNot monotonic
2024-11-28T16:48:25.552817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10265
91.3%
0 63
 
0.6%
0.8333333333 54
 
0.5%
0.9 52
 
0.5%
0.9090909091 48
 
0.4%
0.8 46
 
0.4%
0.5 43
 
0.4%
0.75 38
 
0.3%
0.6666666667 33
 
0.3%
0.8571428571 28
 
0.2%
Other values (177) 574
 
5.1%
ValueCountFrequency (%)
0 63
0.6%
0.1111111111 1
 
< 0.1%
0.2 2
 
< 0.1%
0.25 1
 
< 0.1%
0.2727272727 1
 
< 0.1%
0.3 1
 
< 0.1%
0.3333333333 14
 
0.1%
0.4 3
 
< 0.1%
0.4166666667 1
 
< 0.1%
0.4285714286 1
 
< 0.1%
ValueCountFrequency (%)
1 10265
91.3%
0.9921875 1
 
< 0.1%
0.9916666667 1
 
< 0.1%
0.988372093 4
 
< 0.1%
0.9882352941 2
 
< 0.1%
0.984375 2
 
< 0.1%
0.9830508475 1
 
< 0.1%
0.9827586207 2
 
< 0.1%
0.9814814815 3
 
< 0.1%
0.9807692308 1
 
< 0.1%

Cumulative_Missed_Semesters
Real number (ℝ)

Zeros 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12833511
Minimum0
Maximum13
Zeros10476
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:25.804010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.63292033
Coefficient of variation (CV)4.9317783
Kurtosis104.94014
Mean0.12833511
Median Absolute Deviation (MAD)0
Skewness8.5988893
Sum1443
Variance0.40058814
MonotonicityNot monotonic
2024-11-28T16:48:26.027017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 10476
93.2%
1 454
 
4.0%
2 164
 
1.5%
3 69
 
0.6%
4 38
 
0.3%
5 18
 
0.2%
10 6
 
0.1%
6 5
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 10476
93.2%
1 454
 
4.0%
2 164
 
1.5%
3 69
 
0.6%
4 38
 
0.3%
5 18
 
0.2%
6 5
 
< 0.1%
7 5
 
< 0.1%
8 4
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
12 2
 
< 0.1%
10 6
 
0.1%
9 2
 
< 0.1%
8 4
 
< 0.1%
7 5
 
< 0.1%
6 5
 
< 0.1%
5 18
 
0.2%
4 38
0.3%
3 69
0.6%

Times_returned
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
0.0
10477 
1.0
 
680
2.0
 
78
3.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33732
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 10477
93.2%
1.0 680
 
6.0%
2.0 78
 
0.7%
3.0 9
 
0.1%

Length

2024-11-28T16:48:26.248396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T16:48:26.430045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 10477
93.2%
1.0 680
 
6.0%
2.0 78
 
0.7%
3.0 9
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21721
64.4%
. 11244
33.3%
1 680
 
2.0%
2 78
 
0.2%
3 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22488
66.7%
Other Punctuation 11244
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21721
96.6%
1 680
 
3.0%
2 78
 
0.3%
3 9
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 11244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21721
64.4%
. 11244
33.3%
1 680
 
2.0%
2 78
 
0.2%
3 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21721
64.4%
. 11244
33.3%
1 680
 
2.0%
2 78
 
0.2%
3 9
 
< 0.1%

ESTADO_ESTUDIANTE
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
NO DESERTOR
10052 
DESERTOR
1192 

Length

Max length11
Median length11
Mean length10.681964
Min length8

Characters and Unicode

Total characters120108
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO DESERTOR
2nd rowNO DESERTOR
3rd rowNO DESERTOR
4th rowNO DESERTOR
5th rowNO DESERTOR

Common Values

ValueCountFrequency (%)
NO DESERTOR 10052
89.4%
DESERTOR 1192
 
10.6%

Length

2024-11-28T16:48:26.637017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T16:48:26.823661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
desertor 11244
52.8%
no 10052
47.2%

Most occurring characters

ValueCountFrequency (%)
E 22488
18.7%
R 22488
18.7%
O 21296
17.7%
D 11244
9.4%
S 11244
9.4%
T 11244
9.4%
N 10052
8.4%
10052
8.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 110056
91.6%
Space Separator 10052
 
8.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 22488
20.4%
R 22488
20.4%
O 21296
19.4%
D 11244
10.2%
S 11244
10.2%
T 11244
10.2%
N 10052
9.1%
Space Separator
ValueCountFrequency (%)
10052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 110056
91.6%
Common 10052
 
8.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 22488
20.4%
R 22488
20.4%
O 21296
19.4%
D 11244
10.2%
S 11244
10.2%
T 11244
10.2%
N 10052
9.1%
Common
ValueCountFrequency (%)
10052
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 22488
18.7%
R 22488
18.7%
O 21296
17.7%
D 11244
9.4%
S 11244
9.4%
T 11244
9.4%
N 10052
8.4%
10052
8.4%

CLASIFICACION_BECAS
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size433.7 KiB
OTRAS FORMAS DE PAGO
7353 
BECAS EXTERNAS
2192 
BECAS INTERNAS
1699 

Length

Max length20
Median length20
Mean length17.923693
Min length14

Characters and Unicode

Total characters201534
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOTRAS FORMAS DE PAGO
2nd rowBECAS EXTERNAS
3rd rowOTRAS FORMAS DE PAGO
4th rowOTRAS FORMAS DE PAGO
5th rowOTRAS FORMAS DE PAGO

Common Values

ValueCountFrequency (%)
OTRAS FORMAS DE PAGO 7353
65.4%
BECAS EXTERNAS 2192
 
19.5%
BECAS INTERNAS 1699
 
15.1%

Length

2024-11-28T16:48:27.038827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-28T16:48:27.334565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
otras 7353
19.8%
formas 7353
19.8%
de 7353
19.8%
pago 7353
19.8%
becas 3891
10.5%
externas 2192
 
5.9%
internas 1699
 
4.6%

Most occurring characters

ValueCountFrequency (%)
A 29841
14.8%
25950
12.9%
S 22488
11.2%
O 22059
10.9%
R 18597
9.2%
E 17327
8.6%
T 11244
 
5.6%
G 7353
 
3.6%
P 7353
 
3.6%
D 7353
 
3.6%
Other values (7) 31969
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 175584
87.1%
Space Separator 25950
 
12.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 29841
17.0%
S 22488
12.8%
O 22059
12.6%
R 18597
10.6%
E 17327
9.9%
T 11244
 
6.4%
G 7353
 
4.2%
P 7353
 
4.2%
D 7353
 
4.2%
M 7353
 
4.2%
Other values (6) 24616
14.0%
Space Separator
ValueCountFrequency (%)
25950
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 175584
87.1%
Common 25950
 
12.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 29841
17.0%
S 22488
12.8%
O 22059
12.6%
R 18597
10.6%
E 17327
9.9%
T 11244
 
6.4%
G 7353
 
4.2%
P 7353
 
4.2%
D 7353
 
4.2%
M 7353
 
4.2%
Other values (6) 24616
14.0%
Common
ValueCountFrequency (%)
25950
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201534
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 29841
14.8%
25950
12.9%
S 22488
11.2%
O 22059
10.9%
R 18597
9.2%
E 17327
8.6%
T 11244
 
5.6%
G 7353
 
3.6%
P 7353
 
3.6%
D 7353
 
3.6%
Other values (7) 31969
15.9%

PROMEDIO_GLOBAL_max
Real number (ℝ)

High correlation 

Distinct233
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3116302
Minimum0
Maximum5
Zeros56
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:27.693163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.77
Q14.17
median4.37
Q34.55
95-th percentile4.79
Maximum5
Range5
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.46747033
Coefficient of variation (CV)0.10842078
Kurtosis38.427083
Mean4.3116302
Median Absolute Deviation (MAD)0.19
Skewness-4.8568499
Sum48479.97
Variance0.21852851
MonotonicityNot monotonic
2024-11-28T16:48:27.967802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 233
 
2.1%
4.4 185
 
1.6%
4.45 184
 
1.6%
4.25 170
 
1.5%
4.41 169
 
1.5%
4.47 167
 
1.5%
4.46 164
 
1.5%
4.55 158
 
1.4%
4.35 157
 
1.4%
4.37 157
 
1.4%
Other values (223) 9500
84.5%
ValueCountFrequency (%)
0 56
0.5%
1.5 23
0.2%
1.55 1
 
< 0.1%
1.56 1
 
< 0.1%
1.6 1
 
< 0.1%
1.61 2
 
< 0.1%
1.75 1
 
< 0.1%
1.77 1
 
< 0.1%
1.78 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5 89
0.8%
4.97 4
 
< 0.1%
4.96 3
 
< 0.1%
4.95 9
 
0.1%
4.94 11
 
0.1%
4.93 8
 
0.1%
4.92 14
 
0.1%
4.91 17
 
0.2%
4.9 35
 
0.3%
4.89 16
 
0.1%

PROMEDIO_GLOBAL_min
Real number (ℝ)

High correlation  Zeros 

Distinct252
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1089586
Minimum0
Maximum5
Zeros203
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:28.270764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.4
Q13.99
median4.23
Q34.43
95-th percentile4.68
Maximum5
Range5
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.67521836
Coefficient of variation (CV)0.16432835
Kurtosis22.804728
Mean4.1089586
Median Absolute Deviation (MAD)0.22
Skewness-4.2942377
Sum46201.13
Variance0.45591983
MonotonicityNot monotonic
2024-11-28T16:48:28.561050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203
 
1.8%
4.3 186
 
1.7%
4 180
 
1.6%
4.5 171
 
1.5%
4.25 158
 
1.4%
4.28 157
 
1.4%
4.4 155
 
1.4%
4.24 155
 
1.4%
4.23 154
 
1.4%
4.22 152
 
1.4%
Other values (242) 9573
85.1%
ValueCountFrequency (%)
0 203
1.8%
1.5 23
 
0.2%
1.55 1
 
< 0.1%
1.56 1
 
< 0.1%
1.6 1
 
< 0.1%
1.61 2
 
< 0.1%
1.75 1
 
< 0.1%
1.77 1
 
< 0.1%
1.78 1
 
< 0.1%
1.8 1
 
< 0.1%
ValueCountFrequency (%)
5 16
0.1%
4.99 1
 
< 0.1%
4.95 2
 
< 0.1%
4.94 4
 
< 0.1%
4.92 6
 
0.1%
4.91 3
 
< 0.1%
4.9 13
0.1%
4.89 4
 
< 0.1%
4.88 9
0.1%
4.87 10
0.1%

PCA_max
Real number (ℝ)

High correlation 

Distinct103
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98742418
Minimum0
Maximum1
Zeros63
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:28.831355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.088911348
Coefficient of variation (CV)0.090043722
Kurtosis91.916196
Mean0.98742418
Median Absolute Deviation (MAD)0
Skewness-9.223239
Sum11102.597
Variance0.0079052278
MonotonicityNot monotonic
2024-11-28T16:48:29.107256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10777
95.8%
0 63
 
0.6%
0.5 37
 
0.3%
0.8333333333 30
 
0.3%
0.6666666667 26
 
0.2%
0.9166666667 21
 
0.2%
0.8 20
 
0.2%
0.75 20
 
0.2%
0.9 14
 
0.1%
0.9090909091 14
 
0.1%
Other values (93) 222
 
2.0%
ValueCountFrequency (%)
0 63
0.6%
0.1111111111 1
 
< 0.1%
0.2 2
 
< 0.1%
0.2727272727 1
 
< 0.1%
0.3 1
 
< 0.1%
0.3333333333 10
 
0.1%
0.4 2
 
< 0.1%
0.4166666667 1
 
< 0.1%
0.5 37
0.3%
0.5555555556 2
 
< 0.1%
ValueCountFrequency (%)
1 10777
95.8%
0.9921875 1
 
< 0.1%
0.9916666667 1
 
< 0.1%
0.988372093 4
 
< 0.1%
0.9882352941 2
 
< 0.1%
0.984375 1
 
< 0.1%
0.9830508475 1
 
< 0.1%
0.9827586207 2
 
< 0.1%
0.9814814815 2
 
< 0.1%
0.9784946237 1
 
< 0.1%

PCA_min
Real number (ℝ)

High correlation 

Distinct145
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97455836
Minimum0
Maximum1
Zeros79
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:29.483394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.83333333
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.11295923
Coefficient of variation (CV)0.11590812
Kurtosis43.053618
Mean0.97455836
Median Absolute Deviation (MAD)0
Skewness-6.1328825
Sum10957.934
Variance0.012759788
MonotonicityNot monotonic
2024-11-28T16:48:29.748766image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10265
91.3%
0.5 106
 
0.9%
0 79
 
0.7%
0.8 75
 
0.7%
0.6666666667 65
 
0.6%
0.75 65
 
0.6%
0.8333333333 61
 
0.5%
0.8888888889 39
 
0.3%
0.8571428571 35
 
0.3%
0.9166666667 29
 
0.3%
Other values (135) 425
 
3.8%
ValueCountFrequency (%)
0 79
0.7%
0.1111111111 1
 
< 0.1%
0.2 2
 
< 0.1%
0.25 1
 
< 0.1%
0.2727272727 1
 
< 0.1%
0.3333333333 19
 
0.2%
0.4 3
 
< 0.1%
0.4166666667 1
 
< 0.1%
0.4285714286 1
 
< 0.1%
0.4444444444 2
 
< 0.1%
ValueCountFrequency (%)
1 10265
91.3%
0.984375 1
 
< 0.1%
0.9814814815 2
 
< 0.1%
0.9807692308 1
 
< 0.1%
0.9803921569 1
 
< 0.1%
0.9791666667 3
 
< 0.1%
0.9787234043 1
 
< 0.1%
0.9782608696 1
 
< 0.1%
0.9777777778 2
 
< 0.1%
0.9772727273 1
 
< 0.1%

std_PGA_change
Real number (ℝ)

High correlation  Zeros 

Distinct2843
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070966965
Minimum0
Maximum3.8820162
Zeros5662
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:30.011047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.083864971
95-th percentile0.2192031
Maximum3.8820162
Range3.8820162
Interquartile range (IQR)0.083864971

Descriptive statistics

Standard deviation0.23378884
Coefficient of variation (CV)3.2943334
Kurtosis109.03832
Mean0.070966965
Median Absolute Deviation (MAD)0
Skewness9.7451053
Sum797.95255
Variance0.054657223
MonotonicityNot monotonic
2024-11-28T16:48:30.297144image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5662
50.4%
0.01414213562 53
 
0.5%
0.03535533906 50
 
0.4%
0.01414213562 46
 
0.4%
0.04242640687 45
 
0.4%
0.007071067812 44
 
0.4%
0.02828427125 44
 
0.4%
0.05656854249 43
 
0.4%
0.04242640687 42
 
0.4%
0.02121320344 42
 
0.4%
Other values (2833) 5173
46.0%
ValueCountFrequency (%)
0 5662
50.4%
3.140184917 × 10-162
 
< 0.1%
5.117875267 × 10-161
 
< 0.1%
5.125396028 × 10-161
 
< 0.1%
6.280369835 × 10-1618
 
0.2%
0.004472135955 1
 
< 0.1%
0.004472135955 1
 
< 0.1%
0.004472135955 1
 
< 0.1%
0.005 1
 
< 0.1%
0.005 1
 
< 0.1%
ValueCountFrequency (%)
3.882016229 1
< 0.1%
3.52139177 1
< 0.1%
3.493107499 1
< 0.1%
3.471894296 1
< 0.1%
3.443610024 1
< 0.1%
3.379970414 1
< 0.1%
3.280975465 1
< 0.1%
3.238549058 1
< 0.1%
3.189051583 2
< 0.1%
3.16783838 2
< 0.1%

std_PCA_change
Real number (ℝ)

High correlation  Zeros 

Distinct526
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0043460376
Minimum0
Maximum0.47140452
Zeros10641
Zeros (%)94.6%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:31.162297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.01237293
Maximum0.47140452
Range0.47140452
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.023258627
Coefficient of variation (CV)5.3516855
Kurtosis73.8184
Mean0.0043460376
Median Absolute Deviation (MAD)0
Skewness7.511784
Sum48.866847
Variance0.00054096371
MonotonicityNot monotonic
2024-11-28T16:48:31.439379image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10641
94.6%
0.1414213562 7
 
0.1%
0.1010152545 6
 
0.1%
0.06415002991 5
 
< 0.1%
0.1178511302 4
 
< 0.1%
0.0576458608 4
 
< 0.1%
0.05115939748 4
 
< 0.1%
0.06758625034 4
 
< 0.1%
0.03295154289 4
 
< 0.1%
0.09622504486 4
 
< 0.1%
Other values (516) 561
 
5.0%
ValueCountFrequency (%)
0 10641
94.6%
0.0009903456319 1
 
< 0.1%
0.001225457123 1
 
< 0.1%
0.001609942199 1
 
< 0.1%
0.002208363255 1
 
< 0.1%
0.00367328198 1
 
< 0.1%
0.004037562961 1
 
< 0.1%
0.004324103712 1
 
< 0.1%
0.004608153432 1
 
< 0.1%
0.005356869554 1
 
< 0.1%
ValueCountFrequency (%)
0.4714045208 1
< 0.1%
0.3710456806 1
< 0.1%
0.3706703245 1
< 0.1%
0.3535533906 1
< 0.1%
0.3299831646 1
< 0.1%
0.2929732639 1
< 0.1%
0.2748608182 1
< 0.1%
0.2719641466 1
< 0.1%
0.2710406911 1
< 0.1%
0.2678434777 1
< 0.1%

avg_PGA_trend_per_semester
Real number (ℝ)

Zeros 

Distinct1316
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.019837194
Minimum-2.5
Maximum0.75
Zeros2446
Zeros (%)21.8%
Negative4762
Negative (%)42.4%
Memory size433.7 KiB
2024-11-28T16:48:31.694694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-2.5
5-th percentile-0.11666667
Q1-0.0325
median0
Q30.022125
95-th percentile0.10333333
Maximum0.75
Range3.25
Interquartile range (IQR)0.054625

Descriptive statistics

Standard deviation0.18232935
Coefficient of variation (CV)-9.1912874
Kurtosis88.39171
Mean-0.019837194
Median Absolute Deviation (MAD)0.0275
Skewness-8.4198214
Sum-223.04941
Variance0.033243993
MonotonicityNot monotonic
2024-11-28T16:48:31.952990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2446
 
21.8%
-0.04 130
 
1.2%
0.04 120
 
1.1%
-0.02 117
 
1.0%
-0.01 109
 
1.0%
0.02 107
 
1.0%
-0.005 95
 
0.8%
-0.025 94
 
0.8%
0.01 92
 
0.8%
0.005 90
 
0.8%
Other values (1306) 7844
69.8%
ValueCountFrequency (%)
-2.5 1
< 0.1%
-2.435 1
< 0.1%
-2.41 1
< 0.1%
-2.405 1
< 0.1%
-2.395 1
< 0.1%
-2.39 1
< 0.1%
-2.37 1
< 0.1%
-2.32 1
< 0.1%
-2.3 1
< 0.1%
-2.295 1
< 0.1%
ValueCountFrequency (%)
0.75 1
< 0.1%
0.65 1
< 0.1%
0.565 1
< 0.1%
0.515 2
< 0.1%
0.5 1
< 0.1%
0.48 1
< 0.1%
0.46 1
< 0.1%
0.46 1
< 0.1%
0.42 1
< 0.1%
0.405 1
< 0.1%

avg_PCA_trend_per_semester
Real number (ℝ)

Zeros 

Distinct403
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00045674713
Minimum-0.27777778
Maximum0.33333333
Zeros10428
Zeros (%)92.7%
Negative279
Negative (%)2.5%
Memory size433.7 KiB
2024-11-28T16:48:32.216256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-0.27777778
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.33333333
Range0.61111111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.018451438
Coefficient of variation (CV)40.397491
Kurtosis82.616426
Mean0.00045674713
Median Absolute Deviation (MAD)0
Skewness0.65657312
Sum5.1356647
Variance0.00034045556
MonotonicityNot monotonic
2024-11-28T16:48:32.477466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10428
92.7%
0.125 18
 
0.2%
0.04166666667 17
 
0.2%
0.02777777778 15
 
0.1%
0.02 14
 
0.1%
0.1 12
 
0.1%
0.01818181818 11
 
0.1%
0.02380952381 10
 
0.1%
0.03333333333 10
 
0.1%
0.025 10
 
0.1%
Other values (393) 699
 
6.2%
ValueCountFrequency (%)
-0.2777777778 1
 
< 0.1%
-0.2619047619 1
 
< 0.1%
-0.25 4
< 0.1%
-0.2211538462 1
 
< 0.1%
-0.1866666667 1
 
< 0.1%
-0.1666666667 5
< 0.1%
-0.16 1
 
< 0.1%
-0.15 7
0.1%
-0.1474358974 1
 
< 0.1%
-0.1428571429 1
 
< 0.1%
ValueCountFrequency (%)
0.3333333333 1
 
< 0.1%
0.3 1
 
< 0.1%
0.25 5
< 0.1%
0.2391304348 1
 
< 0.1%
0.2 2
 
< 0.1%
0.1875 1
 
< 0.1%
0.1666666667 7
0.1%
0.1538461538 1
 
< 0.1%
0.1428571429 1
 
< 0.1%
0.1363636364 3
< 0.1%

PORCENTAJE_BECA_INTERNA_CARRERA
Real number (ℝ)

High correlation  Zeros 

Distinct307
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13271627
Minimum0
Maximum1
Zeros8859
Zeros (%)78.8%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:32.746233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2974653
Coefficient of variation (CV)2.2413627
Kurtosis3.0198122
Mean0.13271627
Median Absolute Deviation (MAD)0
Skewness2.1243473
Sum1492.2617
Variance0.088485604
MonotonicityNot monotonic
2024-11-28T16:48:33.038381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8859
78.8%
1 759
 
6.8%
0.5 288
 
2.6%
0.25 90
 
0.8%
0.75 87
 
0.8%
0.6666666667 80
 
0.7%
0.42 74
 
0.7%
0.3333333333 69
 
0.6%
0.8 55
 
0.5%
0.2 48
 
0.4%
Other values (297) 835
 
7.4%
ValueCountFrequency (%)
0 8859
78.8%
0.0025 1
 
< 0.1%
0.005 1
 
< 0.1%
0.01 2
 
< 0.1%
0.01166666667 1
 
< 0.1%
0.012 1
 
< 0.1%
0.0125 1
 
< 0.1%
0.01428571429 1
 
< 0.1%
0.016 4
 
< 0.1%
0.01666666667 6
 
0.1%
ValueCountFrequency (%)
1 759
6.8%
0.975 1
 
< 0.1%
0.97 3
 
< 0.1%
0.9666666667 1
 
< 0.1%
0.962 2
 
< 0.1%
0.9525 7
 
0.1%
0.95 13
 
0.1%
0.95 1
 
< 0.1%
0.9475 1
 
< 0.1%
0.944 3
 
< 0.1%

num_semesters
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8769121
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:33.260463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5189411
Coefficient of variation (CV)0.52797617
Kurtosis0.52486923
Mean2.8769121
Median Absolute Deviation (MAD)1
Skewness0.80742522
Sum32348
Variance2.3071819
MonotonicityNot monotonic
2024-11-28T16:48:33.467055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 3511
31.2%
4 2100
18.7%
1 2094
18.6%
3 1892
16.8%
5 1043
 
9.3%
6 387
 
3.4%
7 147
 
1.3%
8 52
 
0.5%
9 11
 
0.1%
10 3
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 2094
18.6%
2 3511
31.2%
3 1892
16.8%
4 2100
18.7%
5 1043
 
9.3%
6 387
 
3.4%
7 147
 
1.3%
8 52
 
0.5%
9 11
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 3
 
< 0.1%
10 3
 
< 0.1%
9 11
 
0.1%
8 52
 
0.5%
7 147
 
1.3%
6 387
 
3.4%
5 1043
9.3%
4 2100
18.7%
3 1892
16.8%

YEAR_GRADUATION
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.0848
Minimum1975
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size433.7 KiB
2024-11-28T16:48:33.713740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1975
5-th percentile2001
Q12009
median2014
Q32016
95-th percentile2019
Maximum2022
Range47
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.7749999
Coefficient of variation (CV)0.0028701572
Kurtosis2.0141887
Mean2012.0848
Median Absolute Deviation (MAD)3
Skewness-1.2120775
Sum22623882
Variance33.350623
MonotonicityNot monotonic
2024-11-28T16:48:33.970678image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
2015 1369
12.2%
2016 1035
 
9.2%
2014 1005
 
8.9%
2013 841
 
7.5%
2017 838
 
7.5%
2012 768
 
6.8%
2018 588
 
5.2%
2011 578
 
5.1%
2010 478
 
4.3%
2009 446
 
4.0%
Other values (33) 3298
29.3%
ValueCountFrequency (%)
1975 2
 
< 0.1%
1979 2
 
< 0.1%
1980 1
 
< 0.1%
1981 2
 
< 0.1%
1982 1
 
< 0.1%
1985 4
< 0.1%
1986 2
 
< 0.1%
1987 7
0.1%
1988 6
0.1%
1989 6
0.1%
ValueCountFrequency (%)
2022 114
 
1.0%
2021 180
 
1.6%
2020 166
 
1.5%
2019 359
 
3.2%
2018 588
5.2%
2017 838
7.5%
2016 1035
9.2%
2015 1369
12.2%
2014 1005
8.9%
2013 841
7.5%

Interactions

2024-11-28T16:48:18.033672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:28.971236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:32.386404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:35.317197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:38.669820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:42.391273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:45.404823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:48.355553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:51.202420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:58.504242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:01.451088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:04.129849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:06.923227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:09.861358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:14.914323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:18.210566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:29.327683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:32.585757image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:35.502479image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:38.853071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:42.603980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:45.585751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:48.535387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:51.378056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:58.689317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:01.621370image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:04.301317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:07.097281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:10.448217image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:15.191589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:18.380666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:29.536062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:32.778821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:35.703838image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:39.121447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:42.790662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:45.773654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:48.733343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:51.556280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:58.874848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:01.790103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:04.480662image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:07.284574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:10.636020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:15.395115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:18.563216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:29.774194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:32.993261image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:35.887275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:39.376963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:42.983309image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:45.968995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:48.931897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:51.735322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:59.066689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:01.960932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:04.668381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:07.464163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:10.818080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:15.584748image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:18.770836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:30.002753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:33.185359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:36.072382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:39.636701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:43.178897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:46.170696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:49.127192image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:56.284336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:59.265441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:02.146823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:04.868374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:07.656281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:11.015397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:15.831293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:18.949709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:30.243162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:33.364643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:36.252857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:39.882724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:43.365915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:46.349728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:49.304213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:56.503286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:59.483310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:02.333870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:05.049555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:07.893948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:11.197916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:16.054587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:19.127304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:30.487552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:33.567407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:36.435389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:40.121634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:43.553770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:46.536240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:49.494966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:56.733021image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:59.690475image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:02.505067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:05.236536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:08.079189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:11.395606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:16.237881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:19.302469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:30.699194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:33.754931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:36.618774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:40.372999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:43.754601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:46.740532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:49.671556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:56.943813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:59.901630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:02.678341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:05.421963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:08.263684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:11.573950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:16.432746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:19.487952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:30.915915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:33.949951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:36.792589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:40.632315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:43.953906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:46.924298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:49.845059image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:57.170817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:00.098069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:02.848429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:05.605043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:08.447616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:11.776930image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:16.633373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:19.753186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:31.154913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:34.155498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:37.007746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:40.899936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:44.183520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:47.140198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:50.062385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:57.412661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:00.303577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:03.041008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:05.829142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:08.648478image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:11.987659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:16.838920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:19.973354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:31.374800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:34.332418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:37.175009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:41.135915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:44.378056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:47.321023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:50.235847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:57.579624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:00.482347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:03.208240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:06.001075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:08.832890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:12.211155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:17.033869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:20.211978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:31.596055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:34.532222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:37.404411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:41.398412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:44.601842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:47.570241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:50.429303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:57.798509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:00.678201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:03.406938image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:06.191671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:09.063876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:12.402863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:17.234905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:20.449559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:31.836701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:34.730480image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:37.849959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:41.640531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:44.821050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:47.804900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:50.622904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:57.986248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:00.870910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:03.613038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:06.381116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:09.262741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:12.606180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:17.437263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:20.693393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:32.031527image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:34.918486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:38.305100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:41.900201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:45.017881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:47.988038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:50.825210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:58.161796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:01.056710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:03.784914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:06.566602image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:09.474823image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:13.388944image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:17.645148image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:20.917016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:32.209467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:35.134843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:38.482213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:42.106016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:45.208000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:48.166934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:51.031346image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:47:58.336304image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:01.246779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:03.963890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:06.744811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:09.667510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:14.472489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-28T16:48:17.821949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-11-28T16:48:34.219389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
CLASIFICACION_BECASCumulative_Missed_SemestersDEPARTAMENTO_RESIDENCIA_UGEDADESTADO_ESTUDIANTEFACULTAD_PROGRAMA_1PCAPCA_maxPCA_minPORCENTAJE_BECA_INTERNA_CARRERAPROMEDIO_GLOBALPROMEDIO_GLOBAL_maxPROMEDIO_GLOBAL_minTIPO_ESTADO_CIVILTimes_returnedYEAR_GRADUATIONavg_PCA_trend_per_semesteravg_PGA_trend_per_semesternum_semestersstd_PCA_changestd_PGA_change
CLASIFICACION_BECAS1.0000.0270.2650.2760.0610.3890.0360.0350.0280.6870.1320.1170.1210.1640.0380.2570.0170.0330.1050.0000.006
Cumulative_Missed_Semesters0.0271.0000.0000.0210.0710.032-0.151-0.058-0.152-0.024-0.095-0.057-0.1230.0000.476-0.0110.076-0.0210.2590.2110.219
DEPARTAMENTO_RESIDENCIA_UG0.2650.0001.0000.1520.0300.3450.0000.0070.0050.0470.0490.0470.0530.1410.0110.1460.0000.0260.0930.0000.024
EDAD0.2760.0210.1521.0000.0460.149-0.033-0.039-0.034-0.286-0.145-0.102-0.1260.2440.012-0.903-0.0080.0510.1120.0150.065
ESTADO_ESTUDIANTE0.0610.0710.0300.0461.0000.1470.4020.3350.3380.0520.4500.4200.3550.0000.0610.0210.2130.1130.1570.1240.060
FACULTAD_PROGRAMA_10.3890.0320.3450.1490.1471.0000.0360.0360.0510.1290.0910.1020.1010.1430.0540.1410.0330.0430.0780.0300.025
PCA0.036-0.1510.000-0.0330.4020.0361.0000.6840.9990.0160.3430.2760.3460.0000.0900.009-0.285-0.027-0.095-0.752-0.127
PCA_max0.035-0.0580.007-0.0390.3350.0360.6841.0000.6940.0350.2650.2690.2730.0000.0440.0250.4270.0950.039-0.3250.007
PCA_min0.028-0.1520.005-0.0340.3380.0510.9990.6941.0000.0170.3420.2760.3470.0000.0920.010-0.266-0.021-0.098-0.758-0.129
PORCENTAJE_BECA_INTERNA_CARRERA0.687-0.0240.047-0.2860.0520.1290.0160.0350.0171.0000.2090.1900.1680.0480.0490.2940.021-0.0560.0700.0040.032
PROMEDIO_GLOBAL0.132-0.0950.049-0.1450.4500.0910.3430.2650.3420.2091.0000.9150.9090.0250.0660.102-0.075-0.0130.027-0.225-0.039
PROMEDIO_GLOBAL_max0.117-0.0570.047-0.1020.4200.1020.2760.2690.2760.1900.9151.0000.8690.0280.0350.0730.0030.2450.125-0.1520.067
PROMEDIO_GLOBAL_min0.121-0.1230.053-0.1260.3550.1010.3460.2730.3470.1680.9090.8691.0000.0280.0890.087-0.0600.250-0.122-0.241-0.196
TIPO_ESTADO_CIVIL0.1640.0000.1410.2440.0000.1430.0000.0000.0000.0480.0250.0280.0281.0000.0000.2160.0100.0000.0350.0000.000
Times_returned0.0380.4760.0110.0120.0610.0540.0900.0440.0920.0490.0660.0350.0890.0001.0000.0130.0720.0420.2520.1170.054
YEAR_GRADUATION0.257-0.0110.146-0.9030.0210.1410.0090.0250.0100.2940.1020.0730.0870.2160.0131.0000.023-0.035-0.114-0.004-0.059
avg_PCA_trend_per_semester0.0170.0760.000-0.0080.2130.033-0.2850.427-0.2660.021-0.0750.003-0.0600.0100.0720.0231.0000.1850.0900.3330.096
avg_PGA_trend_per_semester0.033-0.0210.0260.0510.1130.043-0.0270.095-0.021-0.056-0.0130.2450.2500.0000.042-0.0350.1851.000-0.0750.012-0.070
num_semesters0.1050.2590.0930.1120.1570.078-0.0950.039-0.0980.0700.0270.125-0.1220.0350.252-0.1140.090-0.0751.0000.2650.835
std_PCA_change0.0000.2110.0000.0150.1240.030-0.752-0.325-0.7580.004-0.225-0.152-0.2410.0000.117-0.0040.3330.0120.2651.0000.304
std_PGA_change0.0060.2190.0240.0650.0600.025-0.1270.007-0.1290.032-0.0390.067-0.1960.0000.054-0.0590.096-0.0700.8350.3041.000

Missing values

2024-11-28T16:48:21.401444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-28T16:48:22.024899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EDADTIPO_ESTADO_CIVILFACULTAD_PROGRAMA_1PROMEDIO_GLOBALDEPARTAMENTO_RESIDENCIA_UGPCACumulative_Missed_SemestersTimes_returnedESTADO_ESTUDIANTECLASIFICACION_BECASPROMEDIO_GLOBAL_maxPROMEDIO_GLOBAL_minPCA_maxPCA_minstd_PGA_changestd_PCA_changeavg_PGA_trend_per_semesteravg_PCA_trend_per_semesterPORCENTAJE_BECA_INTERNA_CARRERAnum_semestersYEAR_GRADUATION
304935.0SOLTERODERECHO4.40BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.404.401.01.0000000.0000000.0000000.0000000.000.000012014.0
402441.0CASADOEDUCACIÓN4.36BOGOTÁ, D.C.1.00.00.0NO DESERTORBECAS EXTERNAS4.364.311.01.0000000.0000000.000000-0.0250000.000.000022007.0
671831.0SOLTERODERECHO4.74BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.744.651.01.0000000.0000000.000000-0.0450000.000.000022018.0
1624038.0SOLTERODERECHO3.76BOGOTÁ, D.C.0.90.00.0NO DESERTOROTRAS FORMAS DE PAGO4.463.761.00.8888890.2254620.0576460.1400000.020.000052013.0
1470142.0SOLTEROINGENIERÍA4.09BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.093.791.01.0000000.0826640.000000-0.0600000.000.000052009.0
182030.0SOLTEROECONOMÍA4.06BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.063.811.01.0000000.0000000.000000-0.1250000.000.000022016.0
886933.0SOLTEROECONOMÍA3.60BOGOTÁ, D.C.1.00.00.0DESERTOROTRAS FORMAS DE PAGO3.603.301.01.0000000.0000000.000000-0.1500000.000.000022014.0
1103632.0SOLTEROCIENCIAS4.64BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.644.391.01.0000000.0971250.000000-0.0625000.000.000042015.0
1518246.0SOLTEROADMINISTRACIÓN3.93BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO3.933.881.01.0000000.0565690.000000-0.0066670.000.000032001.0
47043.0SOLTEROADMINISTRACIÓN4.33FUERA DE BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.334.061.01.0000000.0264580.000000-0.0675000.000.267542007.0
EDADTIPO_ESTADO_CIVILFACULTAD_PROGRAMA_1PROMEDIO_GLOBALDEPARTAMENTO_RESIDENCIA_UGPCACumulative_Missed_SemestersTimes_returnedESTADO_ESTUDIANTECLASIFICACION_BECASPROMEDIO_GLOBAL_maxPROMEDIO_GLOBAL_minPCA_maxPCA_minstd_PGA_changestd_PCA_changeavg_PGA_trend_per_semesteravg_PCA_trend_per_semesterPORCENTAJE_BECA_INTERNA_CARRERAnum_semestersYEAR_GRADUATION
112034.0SOLTEROINGENIERÍA3.81BOGOTÁ, D.C.1.0000001.01.0NO DESERTOROTRAS FORMAS DE PAGO3.853.351.01.0000000.2227550.000000-0.0766670.0000000.33333362013.0
943835.0SOLTEROADMINISTRACIÓN4.16BOGOTÁ, D.C.0.9743590.00.0NO DESERTORBECAS EXTERNAS4.164.061.00.9743590.0424260.018131-0.0333330.0085470.00000032013.0
1185339.0CASADOINGENIERÍA4.72BOGOTÁ, D.C.1.0000000.00.0NO DESERTOROTRAS FORMAS DE PAGO4.724.721.01.0000000.0000000.0000000.0000000.0000000.00000012008.0
1518031.0SOLTEROECONOMÍA4.07BOGOTÁ, D.C.1.0000000.00.0NO DESERTOROTRAS FORMAS DE PAGO4.094.071.01.0000000.0000000.0000000.0066670.0000000.00000032016.0
965234.0SOLTEROINGENIERÍA4.33BOGOTÁ, D.C.1.0000000.00.0NO DESERTORBECAS EXTERNAS4.414.301.01.0000000.0772440.0000000.0040000.0000000.00000052012.0
482541.0SOLTEROCIDER4.70BOGOTÁ, D.C.1.0000000.00.0NO DESERTOROTRAS FORMAS DE PAGO4.704.581.01.0000000.0000000.000000-0.0600000.0000000.00000022013.0
1175434.0SOLTEROEDUCACIÓN4.77BOGOTÁ, D.C.1.0000000.00.0NO DESERTOROTRAS FORMAS DE PAGO5.004.771.01.0000000.0212130.0000000.0766670.0000000.00000032014.0
1422030.0SOLTEROINGENIERÍA4.34BOGOTÁ, D.C.1.0000000.00.0NO DESERTORBECAS INTERNAS4.384.191.01.0000000.0781020.000000-0.0375000.0000000.75000042018.0
1667545.0SOLTEROINGENIERÍA4.25BOGOTÁ, D.C.1.0000000.00.0DESERTOROTRAS FORMAS DE PAGO4.254.251.01.0000000.0000000.0000000.0000000.0000000.00000012004.0
1539428.0SOLTEROCIENCIAS3.70BOGOTÁ, D.C.0.7391300.00.0DESERTOROTRAS FORMAS DE PAGO4.853.701.00.7391300.5293710.1506130.2875000.0652170.25000042016.0

Duplicate rows

Most frequently occurring

EDADTIPO_ESTADO_CIVILFACULTAD_PROGRAMA_1PROMEDIO_GLOBALDEPARTAMENTO_RESIDENCIA_UGPCACumulative_Missed_SemestersTimes_returnedESTADO_ESTUDIANTECLASIFICACION_BECASPROMEDIO_GLOBAL_maxPROMEDIO_GLOBAL_minPCA_maxPCA_minstd_PGA_changestd_PCA_changeavg_PGA_trend_per_semesteravg_PCA_trend_per_semesterPORCENTAJE_BECA_INTERNA_CARRERAnum_semestersYEAR_GRADUATION# duplicates
630.0SOLTEROINGENIERÍA0.00BOGOTÁ, D.C.1.00.00.0DESERTOROTRAS FORMAS DE PAGO0.000.001.01.00.00.00.00.00.012018.03
028.0SOLTEROECONOMÍA4.13BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.134.131.01.00.00.00.00.00.012018.02
128.0SOLTEROECONOMÍA4.44BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.444.441.01.00.00.00.00.00.012019.02
228.0SOLTEROINGENIERÍA4.59BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.594.591.01.00.00.00.00.00.012018.02
329.0SOLTEROECONOMÍA4.21BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.214.211.01.00.00.00.00.00.012017.02
429.0SOLTEROINGENIERÍA4.40BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.404.401.01.00.00.00.00.00.012017.02
530.0SOLTERODERECHO4.44BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.444.441.01.00.00.00.00.00.012018.02
730.0SOLTEROINGENIERÍA4.52BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.524.521.01.00.00.00.00.00.012016.02
831.0SOLTEROINGENIERÍA4.25BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.254.251.01.00.00.00.00.00.012015.02
932.0SOLTEROADMINISTRACIÓN4.61BOGOTÁ, D.C.1.00.00.0NO DESERTOROTRAS FORMAS DE PAGO4.614.611.01.00.00.00.00.00.012015.02